Covid Report for Family

Background

Notes and format last updated May 7, 2020

Starting on the May 7th update, the NY Times began including probable covid cases/deaths along with confirmed. This mostly affects death counts – for certain geographies that include probable COVID deaths in addition to confirmed, these are now added to the totals. For the time being, they were all added to the May 6th totals, causing a big spike at the U.S. level. Over time, NY Times will revise their historical counts and distribute these added deaths when they actually occurred, so the spike should fade.

• This page shows COVID data broken down into regions that affect our family. It pulls from publicly available raw data from the NY Times, which is reported daily. It’s on a one-day lag, so when you see this report, the most recent day included will be that reported the day prior.
• The table of contents above summarizes the major sections and can be used to jump to those sections.
• Each major section is then divided into tabs to allow you to more easily shuffle between geographies and metrics.
  • If you open on your phone, I don’t believe the tabs will work, so you’ll just see the page as one long, continuous document.
• More detailed descriptions and context can be found in each section.

Growth rates

Heat maps

• The two heat maps below compare how quickly total cases or deaths have grown at various times in our respective geopgraphies.
• The first plot compares growth rate for total cases; the second, growth rate for total deaths.
• The metric used is doubling time, by which I mean how quickly total cases or deaths are doubling.
• The plots track that doubling time at each date for our geographies. Darker colors reflect shorter doubling times, and thus periods of faster growth.
  • You can use the plots to track each geography over time and to compare the geographies to one another.
  • You can also compare the cases and death charts, to see how faster periods of death growth follow faster periods of case growth.

Case growth rates

• This section charts the growth rate of both total and new cases for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total cases and new cases.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of cases, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new cases) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new case reporting at the state level.
  • For total cases, the trendlines are a rolling 3-day average of daily growth rates in total cases. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total cases have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total case line get as close to zero as possible.
  • For new cases, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new cases. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total cases, we want to watch for the lines for new cases to get consistently below zero and stay there. That means that we are consistently seeing fewer new cases on a daily basis.

U.S.

Our states

Death growth rates

• This section charts the growth rate of both total and new deaths for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total deaths and new deaths.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of deaths, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new deaths) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new death reporting at the state level.
  • For total deaths, the trendlines are a rolling 3-day average of daily growth rates in total deaths. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total deaths have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total death line get as close to zero as possible.
  • For new deaths, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new deaths. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total deaths, we want to watch for the lines for new deaths to get consistently below zero and stay there. That means that we are consistently seeing fewer new deaths on a daily basis.

U.S.

Our states

By population rankings

This section tracks metrics for states and counties normalized for population (number of cases or deaths per million residents), and then compares these figures both for our geographies and the country overall.

States

• This section shows tables ranking all 50 states for per populations rates of total cases, new cases, total deaths, and new deaths.
• For each metric, in addition to the tables, the trends for the top states are plotted over time.
  • We only plot the top ten states for each metric so that the plots aren’t too crowded. But you can view the full 50-state rankings in the tables.

Total confirmed cases

Table of total confirmed cases per million residents (all 50 states)

Ranking	State	Cases Per Million
1	New York	17,898
2	New Jersey	16,066
3	Massachusetts	11,923
4	Rhode Island	11,342
5	Connecticut	9,947
6	District of Columbia	9,544
7	Delaware	7,417
8	Louisiana	7,203
9	Illinois	6,950
10	Maryland	5,958
11	Michigan	4,955
12	Pennsylvania	4,933
13	Nebraska	4,867
14	Iowa	4,334
15	South Dakota	4,286
16	Indiana	3,955
17	Colorado	3,614
18	Mississippi	3,522
19	Virginia	3,258
20	Georgia	3,242
21	Kansas	2,632
22	New Mexico	2,624
23	Washington	2,490
24	New Hampshire	2,487
25	Tennessee	2,428
26	Minnesota	2,383
27	Alabama	2,264
28	Ohio	2,255
29	North Dakota	2,246
30	Nevada	2,147
31	Utah	2,108
32	Florida	2,011
33	Wisconsin	1,937
34	California	1,896
35	Arizona	1,741
36	Missouri	1,709
37	Kentucky	1,655
38	South Carolina	1,590
39	North Carolina	1,582
40	Texas	1,557
41	Vermont	1,493
42	Arkansas	1,446
43	Idaho	1,316
44	Oklahoma	1,253
45	Wyoming	1,211
46	Maine	1,164
47	Oregon	824
48	West Virginia	800
49	Puerto Rico	759
50	Alaska	529
51	Hawaii	442
52	Montana	432

New confirmed cases

Table of new cases per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Cases Per Million
1	Illinois	235
2	Delaware	225
3	Massachusetts	179
4	Rhode Island	178
5	District of Columbia	163
6	Connecticut	158
7	Nebraska	144
8	Maryland	139
9	Iowa	137
10	Louisiana	120
11	Virginia	107
12	New Jersey	103
13	New York	101
14	Minnesota	97
15	Mississippi	90
16	North Dakota	84
17	Indiana	73
18	New Mexico	68
19	Pennsylvania	67
20	South Dakota	67
21	Michigan	65
22	Alabama	63
23	Georgia	61
24	Arizona	59
25	Colorado	59
26	Kansas	58
27	Tennessee	56
28	New Hampshire	54
29	Kentucky	51
30	Nevada	49
31	Wisconsin	49
32	North Carolina	48
33	Texas	48
34	California	45
35	Ohio	44
36	Utah	41
37	Arkansas	35
38	Florida	34
39	Oklahoma	29
40	Washington	29
41	Maine	25
42	Missouri	25
43	South Carolina	25
44	Wyoming	18
45	Puerto Rico	17
46	Idaho	16
47	Oregon	15
48	West Virginia	12
49	Alaska	3
50	Vermont	3
51	Hawaii	0
52	Montana	0

Total deaths

Table of total deaths per million residents (all 50 states)

Ranking	State	Deaths Per Million
1	New York	1,419
2	New Jersey	1,119
3	Connecticut	902
4	Massachusetts	795
5	District of Columbia	507
6	Louisiana	505
7	Michigan	479
8	Rhode Island	441
9	Pennsylvania	335
10	Illinois	311
11	Maryland	308
12	Delaware	267
13	Indiana	244
14	Colorado	189
15	Mississippi	161
16	Georgia	143
17	Ohio	131
18	Washington	130
19	Minnesota	119
20	New Mexico	115
21	New Hampshire	111
22	Virginia	111
23	Nevada	110
24	Iowa	100
25	Alabama	96
26	Missouri	92
27	Florida	87
28	Arizona	85
29	Vermont	84
30	California	76
31	Kentucky	76
32	Wisconsin	74
33	South Carolina	72
34	Oklahoma	71
35	Kansas	62
36	Nebraska	61
37	North Carolina	60
38	North Dakota	52
39	Maine	51
40	South Dakota	48
41	Texas	43
42	Tennessee	41
43	Idaho	40
44	Puerto Rico	36
45	West Virginia	34
46	Arkansas	32
47	Oregon	32
48	Utah	23
49	Montana	14
50	Hawaii	12
51	Wyoming	12
52	Alaska	10

New deaths

Table of new deaths per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Deaths Per Million
1	New Jersey	23
2	Connecticut	19
3	Massachusetts	18
4	District of Columbia	14
5	Illinois	12
6	Delaware	11
7	Pennsylvania	11
8	Rhode Island	11
9	Maryland	10
10	New York	10
11	Louisiana	7
12	Colorado	6
13	Michigan	6
14	Indiana	5
15	Mississippi	5
16	New Mexico	5
17	Ohio	5
18	Alabama	4
19	Iowa	4
20	Minnesota	4
21	New Hampshire	4
22	Virginia	4
23	Arizona	3
24	Georgia	3
25	Nebraska	3
26	South Dakota	3
27	Florida	2
28	Missouri	2
29	Nevada	2
30	California	1
31	Kentucky	1
32	North Carolina	1
33	North Dakota	1
34	South Carolina	1
35	Tennessee	1
36	Texas	1
37	Washington	1
38	Wisconsin	1
39	Alaska	0
40	Arkansas	0
41	Hawaii	0
42	Idaho	0
43	Kansas	0
44	Maine	0
45	Montana	0
46	Oklahoma	0
47	Oregon	0
48	Puerto Rico	0
49	Utah	0
50	Vermont	0
51	West Virginia	0
52	Wyoming	0

Counties

• This section focuses on the county level. It shows tables with our counties ranked by percentile of U.S. counties for per population rates of total cases and total deaths.
  • Each table also shows the top five counties in the country in addition to our counties, for added perspecive.
• In addition to the tables, our counties’ percentile for both total cases and total deaths are plotted over time.

Confirmed cases

Table showing total cases per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Cases Per Million	Raw Ranking	Percentile
Trousdale	Tennessee	122,474	1	99
Dakota	Nebraska	74,503	2	99
Lincoln	Arkansas	72,865	3	99
Nobles	Minnesota	60,983	4	99
Lake	Tennessee	57,155	5	99
Davidson	Tennessee	5,603	247	92
Richland	South Carolina	2,838	582	81
Pierce	Washington	1,938	842	73
Orange	California	1,267	1170	62
York	South Carolina	968	1426	54

Our county percentiles over time

Deaths

Table showing total deaths per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Deaths Per Million	Raw Ranking	Percentile
Randolph	Georgia	3,098	1	99
Terrell	Georgia	2,813	2	99
Early	Georgia	2,748	3	99
Essex	New Jersey	1,872	4	99
Nassau	New York	1,831	5	99
Richland	South Carolina	144	483	84
Pierce	Washington	72	798	74
Davidson	Tennessee	61	898	71
Orange	California	25	1283	59
York	South Carolina	18	1414	54

Our county percentiles over time

Raw counts

• This section shows the raw counts over time for the U.S., our states, and our counties for four categories:
  • Total confirmed cases: This is the cumulative total of cases reported.
  • New confirmed cases: This is the number of new cases reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new cases, to help better visualize the peak.
  • Total deaths: This is the cumulative total of deaths reported.
  • New deaths: This is the number of new deaths reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new deaths, to help better visualize the peak.

Total confirmed cases

U.S.

Our states

Our counties

New confirmed cases

U.S.

Our states

Our counties

Total deaths

U.S.

Our states

Our counties

New deaths

U.S.

Our states

Our counties

Stay-at-home comparisons

• This section is a work in progress. It’s exploring what effect stay-at-home orders have on spread. It’s inherentely messy because some states have localized orders but not orders for their full state and because states implemented the orders at different times.
  • The first plot shows a rolling 3-day average of new cases per million residents, averaged across 2 groups of states – those with a state-wide stay-at-home order in effect and those without. The size of the dots reflect the number of states in each group for each given day (so the dots are getting bigger over time for the group with orders, as more states implement those orders).
    • So far, this view doesn’t show if these orders are “working” or not. Instead, the biggest takeaway from the current plot is that the states that implemented orders are reacting to having worse spread, which is why the case rate is so much higher at the start for them. As a state decides it has a problem and implements an order, it joins the grouping of states with orders and drives the rate up for that group.
    • Over time (if the policies work), what we’d expect is that the line for the group with orders flattens or declines in relation to the no-order group. It also may become moot if all states issue orders.
    • As states start rescending their orders, I’ll put them back into the no-order group, so the no-order group should beging to expand again.
  • The second plot tracks progress of states by the number of days since orders have been in effect. It includes only those states with orders and shows their average new case growth rate (a rolling 3-day average of daily growth rates) based on how long it’s been since the order went into effect. Again, the size of the dots show how many states are included in each point – i.e., as you go further right, the dots are smaller because less states are that far past their order.
    • I’m working on how to add a baseline to this chart to compare to states without orders, but that’s challenging because there’s no way to fully align timing.